Intelligent customer oriented mobility network engineering at edges

ABSTRACT

Aspects of the subject disclosure may include, for example, a method in which a processing system identifies a set of target users of user equipment communication devices (UEs), based on reports from the UEs regarding a quality of service (QoS) experienced by the respective UEs; obtaining from internal sources a set of key performance indicators (KPIs) for the communication network; correlating information received from external sources with the data obtained from the internal sources to validate the reports from the UEs; and recommending, in accordance with data records generated by the correlating, an action to improve the QoS for a UE of the set of UEs, where the action includes a modification of the UE and/or a reconfiguration of the network. Other embodiments are disclosed.

FIELD OF THE DISCLOSURE

The subject disclosure relates to a communication network supporting mobile devices, and more particularly to a system for providing reliable communications for each customer without degrading the quality of service (QoS) for other customers.

BACKGROUND

Performance of a communication network can be measured by monitoring key performance indicators (KPIs) on the network. Network performance data can be collected from various sources and analyzed in near real time. In order to provide a high QoS level to all customers, it is desirable to adaptively optimize and troubleshoot the network based on current performance data.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an exemplary, non-limiting embodiment of a communications network in accordance with various aspects described herein.

FIG. 2A is a block diagram illustrating an example, non-limiting embodiment of a system functioning within the communication network of FIG. 1 in accordance with various aspects described herein.

FIG. 2B schematically illustrates a system for intelligent monitoring, analysis, and optimization of a communication network, in accordance with embodiments of the disclosure.

FIG. 2C schematically illustrates a procedure for adaptive network optimization based on target user behavior and feedback from equipment of target users (UEs), in accordance with embodiments of the disclosure.

FIG. 2D schematically illustrates a procedure for target user event reporting, in accordance with embodiments of the disclosure.

FIG. 2E schematically illustrates a procedure for detecting abnormalities in equipment of target users, in accordance with embodiments of the disclosure.

FIG. 2F schematically illustrates a procedure for network reconfiguration and troubleshooting, in accordance with embodiments of the disclosure.

FIG. 2G schematically illustrates a procedure for assigning network resources to improve customer QoS, in accordance with embodiments of the disclosure.

FIG. 2H schematically illustrates a procedure in which network data and models for network troubleshooting and management are shared or offered for sale, in accordance with embodiments of the disclosure.

FIG. 3 is a block diagram illustrating an example, non-limiting embodiment of a virtualized communication network in accordance with various aspects described herein.

FIG. 4 is a block diagram of an example, non-limiting embodiment of a computing environment in accordance with various aspects described herein.

FIG. 5 is a block diagram of an example, non-limiting embodiment of a mobile network platform in accordance with various aspects described herein.

FIG. 6 is a block diagram of an example, non-limiting embodiment of a communication device in accordance with various aspects described herein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrative embodiments for recommending adaptive action in near real time to improve QoS on a communication network, based on data obtained from both internal and external sources. Other embodiments are described in the subject disclosure.

One or more aspects of the subject disclosure include a method in which a processing system including a processor identifies a set of target users of a communication network having associated therewith a set of user equipment communication devices (UEs); the target users are identified based on reports from the UEs regarding a quality of service (QoS) experienced by the respective UEs, user input received via the UEs, information regarding the target users received from external sources that are external to the processing system, or a combination thereof. The method also includes obtaining, from sources internal to the communication network, data for the communication network comprising a set of key performance indicators (KPIs), and correlating the information received from the external sources with the data obtained from the internal sources to validate the reports from the UEs, resulting in correlated data for each of the UEs. The method further includes generating, from the correlated data, data records relating to the respective UEs, and recommending, in accordance with the data records, an action to improve the QoS for a UE of the set of UEs; the action comprises a modification of the UE and/or a reconfiguration of the network. The method also includes informing, via the respective UEs, the target users regarding the action; receiving communications from at least a portion of the set of UEs regarding the action; and analyzing the communications to provide a revision of the recommended action using a machine learning model generated at least in part using artificial intelligence techniques.

One or more aspects of the subject disclosure include a device that comprises a processing system including a network edge processor of a communication network, and a memory that stores instructions. The instructions, when executed by the processing system, facilitate performance of operations. The operations include identifying a set of target users of a communication network having associated therewith a set of user equipment communication devices (UEs); the target users are identified based on reports from the UEs regarding a quality of service (QoS) experienced by the respective UEs, user input received via the UEs, information regarding the target users received from external sources that are external to the processing system, or a combination thereof. The operations also include obtaining, from sources internal to the communication network, data for the communication network comprising a set of key performance indicators (KPIs), and correlating the information received from the external sources with the data obtained from the internal sources to validate the reports from the UEs, resulting in correlated data for each of the UEs. The operations also include generating, from the correlated data, data records relating to the respective UEs, and recommending, in accordance with the data records, an action to improve the QoS for a UE of the set of UEs; the action comprises a modification of the UE and/or a reconfiguration of the network. The operations also include informing, via the respective UEs, the target users regarding the action; receiving communications from at least a portion of the set of UEs regarding the action; and analyzing the communications to provide a revision of the recommended action using a machine learning model generated at least in part using artificial intelligence techniques.

One or more aspects of the subject disclosure include a non-transitory machine-readable medium comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations. The operations include identifying a set of target users of a communication network having associated therewith a set of user equipment communication devices (UEs); the target users are identified based on reports from the UEs regarding a quality of service (QoS) experienced by the respective UEs, user input received via the UEs, information regarding the target users received from external sources that are external to the processing system, or a combination thereof. The operations also include obtaining, from sources internal to the communication network, data for the communication network comprising a set of key performance indicators (KPIs), and correlating the information received from the external sources with the data obtained from the internal sources to validate the reports from the UEs, resulting in correlated data for each of the UEs. The operations also include generating, from the correlated data, data records relating to the respective UEs; storing the data records in a graph database; and recommending, in accordance with the data records, an action to improve the QoS for a UE of the set of UEs; the action comprises a modification of the UE and/or a reconfiguration of the network. The operations also include informing, via the respective UEs, the target users regarding the action; receiving communications from at least a portion of the set of UEs regarding the action; and analyzing the communications to provide a revision of the recommended action using a machine learning model generated at least in part using artificial intelligence techniques.

Referring now to FIG. 1 , a block diagram is shown illustrating an example, non-limiting embodiment of a system 100 in accordance with various aspects described herein. For example, system 100 can facilitate in whole or in part identifying a set of target users of user equipment communication devices (UEs), based on reports from the UEs regarding a quality of service (QoS) experienced by the respective UEs; obtaining from internal sources a set of key performance indicators (KPIs) for the communication network; correlating information received from external sources with the data obtained from the internal sources to validate the reports from the UEs; and recommending, in accordance with data records generated by the correlating, an action to improve the QoS for a UE of the set of UEs, where the action includes a modification of the UE and/or a reconfiguration of the network. In particular, a communications network 125 is presented for providing broadband access 110 to a plurality of data terminals 114 via access terminal 112, wireless access 120 to a plurality of mobile devices 124 and vehicle 126 via base station or access point 122, voice access 130 to a plurality of telephony devices 134, via switching device 132 and/or media access 140 to a plurality of audio/video display devices 144 via media terminal 142. In addition, communication network 125 is coupled to one or more content sources 175 of audio, video, graphics, text and/or other media. While broadband access 110, wireless access 120, voice access 130 and media access 140 are shown separately, one or more of these forms of access can be combined to provide multiple access services to a single client device (e.g., mobile devices 124 can receive media content via media terminal 142, data terminal 114 can be provided voice access via switching device 132, and so on).

The communications network 125 includes a plurality of network elements (NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110, wireless access 120, voice access 130, media access 140 and/or the distribution of content from content sources 175. The communications network 125 can include a circuit switched or packet switched network, a voice over Internet protocol (VoIP) network, Internet protocol (IP) network, a cable network, a passive or active optical network, a 4G, 5G, or higher generation wireless access network, WIMAX network, UltraWideband network, personal area network or other wireless access network, a broadcast satellite network and/or other communications network.

In various embodiments, the access terminal 112 can include a digital subscriber line access multiplexer (DSLAM), cable modem termination system (CMTS), optical line terminal (OLT) and/or other access terminal. The data terminals 114 can include personal computers, laptop computers, netbook computers, tablets or other computing devices along with digital subscriber line (DSL) modems, data over coax service interface specification (DOCSIS) modems or other cable modems, a wireless modem such as a 4G, 5G, or higher generation modem, an optical modem and/or other access devices.

In various embodiments, the base station or access point 122 can include a 4G, 5G, or higher generation base station, an access point that operates via an 802.11 standard such as 802.11n, 802.11ac or other wireless access terminal. The mobile devices 124 can include mobile phones, e-readers, tablets, phablets, wireless modems, and/or other mobile computing devices.

In various embodiments, the switching device 132 can include a private branch exchange or central office switch, a media services gateway, VoIP gateway or other gateway device and/or other switching device. The telephony devices 134 can include traditional telephones (with or without a terminal adapter), VoIP telephones and/or other telephony devices.

In various embodiments, the media terminal 142 can include a cable head-end or other TV head-end, a satellite receiver, gateway or other media terminal 142. The display devices 144 can include televisions with or without a set top box, personal computers and/or other display devices.

In various embodiments, the content sources 175 include broadcast television and radio sources, video on demand platforms and streaming video and audio services platforms, one or more content data networks, data servers, web servers and other content servers, and/or other sources of media.

In various embodiments, the communications network 125 can include wired, optical and/or wireless links and the network elements 150, 152, 154, 156, etc. can include service switching points, signal transfer points, service control points, network gateways, media distribution hubs, servers, firewalls, routers, edge devices, switches and other network nodes for routing and controlling communications traffic over wired, optical and wireless links as part of the Internet and other public networks as well as one or more private networks, for managing subscriber access, for billing and network management and for supporting other network functions.

FIG. 2A is a block diagram illustrating an example, non-limiting embodiment of a system 201 functioning within the communication network of FIG. 1 in accordance with various aspects described herein. A group of network subscribers 211, using devices 211-1, 211-2, 211-3 respectively, communicate via network 215 (communicating with each other, with other subscribers, with content providers, etc.). In general, network 215 supports communication by a large number of users having a variety of devices and a variety of different needs.

System 210, in communication with network 215 and thus with subscribers 211, collects information regarding performance of the subscribers' devices (also referred to herein as user equipment or UEs) and performance of the network. In an embodiment, system 210 obtains user feedback 212 regarding the quality of service (QoS) experienced by the various UEs, reports of key performance indicators (KPIs) 214 regarding performance of the network, and information from other external sources 213 (e.g. environmental sensors). Examples of KPIs include network throughput, network flow size, network latencies, network retainability and accessibility, reference signal received power (RSRP), reference signal received quality (RSRQ), etc.

System 210 includes an engine 217 that performs monitoring, analysis and optimization of the network, and a set of models 218 based on machine learning and artificial intelligence (ML/AI) for analyzing network performance in a variety of network configurations. System 210 can provide recommendations 216 for various engineering actions to improve QoS for one or more subscribers; in this embodiment, the recommendations are made in near real time based on the collected information.

In this embodiment, system 210 selects the subscribers 211 as a set of target users, collects relevant data and network KPIs for the set of target users, processes the collected data, and adaptively recommends optimized network engineering actions. In a particular embodiment, data collection and processing are performed at the edges of network 215, which may be a 4G/LTE network or a 5G network. The collected information is used to build the models 218 for troubleshooting and optimizing the network.

FIG. 2B schematically illustrates a system 202 for intelligent monitoring, analysis, and optimization of a communication network, in accordance with embodiments of the disclosure. System 202 includes a network engineering system 220 that receives data 222 regarding the target customers 211 from sources external to the network (e.g. posts on social networks by the target customers) as well as data 223 from internal sources, e.g. customer input via dedicated applications (“apps”), or information regarding a specific device used by the customer. System 220 can also receive input 224 from streaming event and mediation (STEM) applications regarding the behavior of the customer devices (UEs). In addition, system 220 can receive network data 225 (e.g. KPIs), direct user input via access portal 226, and other data 227 from external sources (e.g. environmental data).

As shown in FIG. 2B, components of system 220 can perform functions such as combining 291 the customer data from internal and external sources, and filtering/validating 292 and correlating/aggregating 293 the data from various sources. This data is stored in internal database 295 and used by the optimization engine 290 to improve QoS on the network and/or for specific customers. A module 296 provides recommendations for specific actions to reconfigure and/or troubleshoot the network 215.

FIG. 2C schematically illustrates a procedure 203 for adaptive network optimization based on target user behavior and feedback from equipment of target users (UEs), in accordance with embodiments of the disclosure. In step 2302, a set of target customers/users is identified by one or more of several methods, including: (1) direct input from internal apps (e.g. “Mark the Spot” from AT&T) or other dedicated apps; (2) network monitoring for information on a specific model of device, or for abnormal/malicious behavior by specific users (e.g. users with a high number of lost connections); (3) monitoring external sources (e.g. social networks) to find customers with complaints; (4) users associated with previously identified targets or target UEs.

Information and network measurements (e.g. KPIs) regarding the UEs of the identified target users are collected from different data sources (step 2304). In an embodiment, input from target users and feedback from target users are validated (step 2306) by correlating the user input with internal information (including KPIs), and appropriate weights are assigned to the user inputs. Data from multiple data events and/or different sources can be correlated and aggregated for a UE using a set of unique keys, e.g. based on the international mobile subscriber identity (IMSI) for the UE, to generate data records for that UE. The data records are then stored in an internal database (step 2308). In an embodiment, the database is a graph database (e.g. Neo4) where graph analytics can be applied to obtain insights on the behavior of the target users (as evidenced by data from the UE and regarding the UE) and on the operation of the network.

The data records are analyzed by the network optimization engine (step 2310) to diagnose a network condition and/or provide guidance (or generate alarms) to network operators. The adaptive action module recommends specific actions to address any issues identified by the optimization engine (step 2312). The target users can then be informed that the network condition has been resolved (step 2314).

In this embodiment, user feedback (generally, feedback from all users affected by the recommended actions) is collected and analyzed to optimize the actions and improve the QoS for the users. In particular, reinforcement learning ML/AI models can be used to adaptively optimize the actions and allocate resources (step 2316).

It will be appreciated that various functions of system 220 can be performed using network edge processing, to reduce communication latency on network 215. Examples of edge processing include (i) selecting particular records (e.g. rows and/or columns of logged files) based on a key or filename, (ii) filtering data to remove noise and/or interference, and (iii) extracting particular information that can be used in making a decision. Furthermore, the data collected and actions taken, together with historical data regarding performance of the network, can be used as ground truth data for building ML/AI models for intelligent and automatic network troubleshooting and management.

FIG. 2D schematically illustrates a procedure 204 for target user event reporting, in accordance with embodiments of the disclosure. In an embodiment, system 220 identifies a target user and a UE associated with that target user (step 2401), and collects data (including event data) relating to the UE over a specific past time interval (step 2402). This data can include a geolocation (approximate latitude/longitude) of the UE, records of cells visited by the UE, and records of one or more of a UE context release, a UE measurement, a radio resource control (RRC) measurement, and a resource block (RB) traffic measurement. In addition, the system can collect network KPI data (step 2404) for the time interval (e.g. five minutes).

In this embodiment, the collected historical data is sorted by time, reported and visualized; at the conclusion of the time interval (step 2406), the data can be added to a time series associated with the UE and the target user (step 2408). The data records are stored in the internal database (step 2410), and thus can form a fine-grained historical data source for further analysis.

FIG. 2E schematically illustrates a procedure 205 for detecting abnormalities in equipment of target users, in accordance with embodiments of the disclosure. In step 2502, the system 220 collects data regarding signaling, measurement and traffic events for a set of UEs, and then identifies a set of UEs with potential abnormal behavior (step 2504), based on records of an unusually large number of lost connections, radio-link failure, and/or UE capability enquiry timeout. The data records are then stored (step 2506).

The system correlates and summarizes the data records (step 2508); in an embodiment, this is done using specific keys, e.g. IMSI, enbslapid, or global-cell_id. Data from other sources can be added to the summarized data records (step 2510) to provide more insight regarding UE performance. For example, information related to cells traversed by the UE in a past time interval (such as utilization, accessibility and/or retainability of the cells) can be added to the data records. The data is analyzed (step 2512) to diagnose a problem and reliably detect a UE with abnormalities. For instance, if the coverage of the traversed cells is good and there is little network congestion, it is relatively more likely that the UE has an abnormality; an appropriate action can be taken (e.g. upgrading software for the UE, alerting a device team).

FIG. 2F schematically illustrates a procedure 206 in which a processing system performs network reconfiguration and troubleshooting, in accordance with embodiments of the disclosure. In step 2602, UEs identified as having problems/abnormalities are classified according to the reported type of problem or abnormality. Internal databases are queried (step 2604) to determine the locations of the identified UEs (identifiers of cells visited and/or approximate latitude/longitude of the UEs), and to obtain a set of relevant network KPIs based on the type of the reported problem or abnormality. The trajectories of the UEs (which provide expected UE locations) may be constructed to identify network issues (step 2606).

The system then can diagnose the problem (or correct the abnormality) based on the classification of the problem, the UE location and the UE trajectory (step 2608). For example, if a set of UEs is experiencing relatively low levels of reference signal received power (RSRP), reference signal received quality (RSRQ), and/or channel quality indicator (CQI) in specific geographical area and there is a relatively high number of handovers (HOs) among neighboring cells, that area may then be identified as coverage hole. The system can recommend (step 2610) actions to adjust operation of the UE's current cell (and/or neighboring cells, based on the trajectory) or adjust the configuration of the network.

For example, if an area is identified as a coverage hole, the system can remotely adjust the power and/or antenna tilt for a base station/node in the area, or add a node in the area. In another instance, if the RSRP is good but there are a relatively large number of failed sessions and the ratio of uplink throughput to downlink throughput (UL/DL) is low, the system can proceed to examine the cell(s) for a possible internal or external passive intermodulation (PIM) interference problem. In an embodiment, an external user (e.g. a police department) may use system's portal (after appropriate authorization and authentication) to access the historical locations/trajectories of a user.

FIG. 2G schematically illustrates a procedure 207 for assigning network resources to improve customer QoS, in accordance with embodiments of the disclosure. In an embodiment, the system receives a report of UE performance, or feedback from the UE regarding an event involving the UE (step 2702). The system determines the QoS for the UE, and analyzes the available resources (step 2704). If the QoS for a particular UE is deficient (step 2706), the network optimization engine assigns available resources among the UEs (step 2708), to provide reliable communications for a customer experiencing a poor QoS without degrading the QoS for other customers. For example, if a UE reports experiencing a poor quality of service in a healthy cell with high utilization, the system can recommend handing over some other UEs to neighboring cells, or constructing stronger beamforming for that UE.

By developing reinforcement learning ML/AI models (step 2705), feedback from UEs can be analyzed to adaptively optimize the actions and allocate resources to satisfy user expectations (step 2707). This can be of particular importance for particular groups of network subscribers such as first responders and FirstNet users; in an embodiment, the system maintains continuous interaction with first responder UEs to assure a consistently high level of QoS.

FIG. 2H schematically illustrates a procedure 208 in which network data and models for network troubleshooting and management are shared or offered for sale, in accordance with embodiments of the disclosure. In an embodiment, data 2801 collected by system 220 is used (step 2802) to develop intelligent ML/AI models 2803 for network reconfiguration, troubleshooting and resource optimization. The system then performs processing (e.g. including anonymization) to produce (step 2804) processed data 2811 and developed models 2813, which can be appropriately shared or sold to vendors or network device manufacturers. Such real network data or practical models are sought after by vendors and network device manufacturers in developing smart network devices that can operate in highly dynamic/agile network environments (e.g. 5G networks), and react to the network in real time.

While for purposes of simplicity of explanation, the respective methods and procedures are shown and described as a series of blocks in FIGS. 2C-2H, it is to be understood and appreciated that the claimed subject matter is not limited by the order of the blocks, as some blocks may occur in different orders and/or concurrently with other blocks from what is depicted and described herein. Moreover, not all illustrated blocks may be required to implement the methods described herein.

Referring now to FIG. 3 , a block diagram 300 is shown illustrating an example, non-limiting embodiment of a virtualized communication network in accordance with various aspects described herein. In particular a virtualized communication network is presented that can be used to implement some or all of the subsystems and functions of system 100, the subsystems and functions of system 200, and method 230 presented in FIGS. 1, 2A, 2B, 2C, and 3 . For example, virtualized communication network 300 can facilitate in whole or in part identifying a set of target users of user equipment communication devices (UEs), based on reports from the UEs regarding a quality of service (QoS) experienced by the respective UEs; obtaining from internal sources a set of key performance indicators (KPIs) for the communication network; correlating information received from external sources with the data obtained from the internal sources to validate the reports from the UEs; and recommending, in accordance with data records generated by the correlating, an action to improve the QoS for a UE of the set of UEs, where the action includes a modification of the UE and/or a reconfiguration of the network.

In particular, a cloud networking architecture is shown that leverages cloud technologies and supports rapid innovation and scalability via a transport layer 350, a virtualized network function cloud 325 and/or one or more cloud computing environments 375. In various embodiments, this cloud networking architecture is an open architecture that leverages application programming interfaces (APIs); reduces complexity from services and operations; supports more nimble business models; and rapidly and seamlessly scales to meet evolving customer requirements including traffic growth, diversity of traffic types, and diversity of performance and reliability expectations.

In contrast to traditional network elements—which are typically integrated to perform a single function, the virtualized communication network employs virtual network elements (VNEs) 330, 332, 334, etc. that perform some or all of the functions of network elements 150, 152, 154, 156, etc. For example, the network architecture can provide a substrate of networking capability, often called Network Function Virtualization Infrastructure (NFVI) or simply infrastructure that is capable of being directed with software and Software Defined Networking (SDN) protocols to perform a broad variety of network functions and services. This infrastructure can include several types of substrates. The most typical type of substrate being servers that support Network Function Virtualization (NFV), followed by packet forwarding capabilities based on generic computing resources, with specialized network technologies brought to bear when general purpose processors or general purpose integrated circuit devices offered by merchants (referred to herein as merchant silicon) are not appropriate. In this case, communication services can be implemented as cloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1 ), such as an edge router can be implemented via a VNE 330 composed of NFV software modules, merchant silicon, and associated controllers. The software can be written so that increasing workload consumes incremental resources from a common resource pool, and moreover so that it's elastic: so the resources are only consumed when needed. In a similar fashion, other network elements such as other routers, switches, edge caches, and middle-boxes are instantiated from the common resource pool. Such sharing of infrastructure across a broad set of uses makes planning and growing infrastructure easier to manage.

In an embodiment, the transport layer 350 includes fiber, cable, wired and/or wireless transport elements, network elements and interfaces to provide broadband access 110, wireless access 120, voice access 130, media access 140 and/or access to content sources 175 for distribution of content to any or all of the access technologies. In particular, in some cases a network element needs to be positioned at a specific place, and this allows for less sharing of common infrastructure. Other times, the network elements have specific physical layer adapters that cannot be abstracted or virtualized, and might require special DSP code and analog front-ends (AFEs) that do not lend themselves to implementation as VNEs 330, 332 or 334. These network elements can be included in transport layer 350.

The virtualized network function cloud 325 interfaces with the transport layer 350 to provide the VNEs 330, 332, 334, etc. to provide specific NFVs. In particular, the virtualized network function cloud 325 leverages cloud operations, applications, and architectures to support networking workloads. The virtualized network elements 330, 332 and 334 can employ network function software that provides either a one-for-one mapping of traditional network element function or alternately some combination of network functions designed for cloud computing. For example, VNEs 330, 332 and 334 can include route reflectors, domain name system (DNS) servers, and dynamic host configuration protocol (DHCP) servers, system architecture evolution (SAE) and/or mobility management entity (MME) gateways, broadband network gateways, IP edge routers for IP-VPN, Ethernet and other services, load balancers, distributers and other network elements. Because these elements don't typically need to forward large amounts of traffic, their workload can be distributed across a number of servers—each of which adds a portion of the capability, and overall which creates an elastic function with higher availability than its former monolithic version. These virtual network elements 330, 332, 334, etc. can be instantiated and managed using an orchestration approach similar to those used in cloud compute services.

The cloud computing environments 375 can interface with the virtualized network function cloud 325 via APIs that expose functional capabilities of the VNEs 330, 332, 334, etc. to provide the flexible and expanded capabilities to the virtualized network function cloud 325. In particular, network workloads may have applications distributed across the virtualized network function cloud 325 and cloud computing environment 375 and in the commercial cloud, or might simply orchestrate workloads supported entirely in NFV infrastructure from these third party locations.

Turning now to FIG. 4 , there is illustrated a block diagram of a computing environment in accordance with various aspects described herein. In order to provide additional context for various embodiments of the embodiments described herein, FIG. 4 and the following discussion are intended to provide a brief, general description of a suitable computing environment 400 in which the various embodiments of the subject disclosure can be implemented. In particular, computing environment 400 can be used in the implementation of network elements 150, 152, 154, 156, access terminal 112, base station or access point 122, switching device 132, media terminal 142, and/or VNEs 330, 332, 334, etc. Each of these devices can be implemented via computer-executable instructions that can run on one or more computers, and/or in combination with other program modules and/or as a combination of hardware and software. For example, computing environment 400 can facilitate in whole or in part identifying a set of target users of user equipment communication devices (UEs), based on reports from the UEs regarding a quality of service (QoS) experienced by the respective UEs; obtaining from internal sources a set of key performance indicators (KPIs) for the communication network; correlating information received from external sources with the data obtained from the internal sources to validate the reports from the UEs; and recommending, in accordance with data records generated by the correlating, an action to improve the QoS for a UE of the set of UEs, where the action includes a modification of the UE and/or a reconfiguration of the network.

Generally, program modules comprise routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the methods can be practiced with other computer system configurations, comprising single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors as well as other application specific circuits such as an application specific integrated circuit, digital logic circuit, state machine, programmable gate array or other circuit that processes input signals or data and that produces output signals or data in response thereto. It should be noted that while any functions and features described herein in association with the operation of a processor could likewise be performed by a processing circuit.

The illustrated embodiments of the embodiments herein can be also practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which can comprise computer-readable storage media and/or communications media, which two terms are used herein differently from one another as follows. Computer-readable storage media can be any available storage media that can be accessed by the computer and comprises both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable storage media can be implemented in connection with any method or technology for storage of information such as computer-readable instructions, program modules, structured data or unstructured data.

Computer-readable storage media can comprise, but are not limited to, random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read only memory (CD-ROM), digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or other tangible and/or non-transitory media which can be used to store desired information. In this regard, the terms “tangible” or “non-transitory” herein as applied to storage, memory or computer-readable media, are to be understood to exclude only propagating transitory signals per se as modifiers and do not relinquish rights to all standard storage, memory or computer-readable media that are not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via access requests, queries or other data retrieval protocols, for a variety of operations with respect to the information stored by the medium.

Communications media typically embody computer-readable instructions, data structures, program modules or other structured or unstructured data in a data signal such as a modulated data signal, e.g., a carrier wave or other transport mechanism, and comprises any information delivery or transport media. The term “modulated data signal” or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example, and not limitation, communication media comprise wired media, such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.

With reference again to FIG. 4 , the example environment can comprise a computer 402, the computer 402 comprising a processing unit 404, a system memory 406 and a system bus 408. The system bus 408 couples system components including, but not limited to, the system memory 406 to the processing unit 404. The processing unit 404 can be any of various commercially available processors. Dual microprocessors and other multiprocessor architectures can also be employed as the processing unit 404.

The system bus 408 can be any of several types of bus structure that can further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory 406 comprises ROM 410 and RAM 412. A basic input/output system (BIOS) can be stored in a non-volatile memory such as ROM, erasable programmable read only memory (EPROM), EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer 402, such as during startup. The RAM 412 can also comprise a high-speed RAM such as static RAM for caching data.

The computer 402 further comprises an internal hard disk drive (HDD) 414 (e.g., EIDE, SATA), which internal HDD 414 can also be configured for external use in a suitable chassis (not shown), a magnetic floppy disk drive (FDD) 416, (e.g., to read from or write to a removable diskette 418) and an optical disk drive 420, (e.g., reading a CD-ROM disk 422 or, to read from or write to other high capacity optical media such as the DVD). The HDD 414, magnetic FDD 416 and optical disk drive 420 can be connected to the system bus 408 by a hard disk drive interface 424, a magnetic disk drive interface 426 and an optical drive interface 428, respectively. The hard disk drive interface 424 for external drive implementations comprises at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394 interface technologies. Other external drive connection technologies are within contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For the computer 402, the drives and storage media accommodate the storage of any data in a suitable digital format. Although the description of computer-readable storage media above refers to a hard disk drive (HDD), a removable magnetic diskette, and a removable optical media such as a CD or DVD, it should be appreciated by those skilled in the art that other types of storage media which are readable by a computer, such as zip drives, magnetic cassettes, flash memory cards, cartridges, and the like, can also be used in the example operating environment, and further, that any such storage media can contain computer-executable instructions for performing the methods described herein.

A number of program modules can be stored in the drives and RAM 412, comprising an operating system 430, one or more application programs 432, other program modules 434 and program data 436. All or portions of the operating system, applications, modules, and/or data can also be cached in the RAM 412. The systems and methods described herein can be implemented utilizing various commercially available operating systems or combinations of operating systems.

A user can enter commands and information into the computer 402 through one or more wired/wireless input devices, e.g., a keyboard 438 and a pointing device, such as a mouse 440. Other input devices (not shown) can comprise a microphone, an infrared (IR) remote control, a joystick, a game pad, a stylus pen, touch screen or the like. These and other input devices are often connected to the processing unit 404 through an input device interface 442 that can be coupled to the system bus 408, but can be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a universal serial bus (USB) port, an IR interface, etc.

A monitor 444 or other type of display device can be also connected to the system bus 408 via an interface, such as a video adapter 446. It will also be appreciated that in alternative embodiments, a monitor 444 can also be any display device (e.g., another computer having a display, a smart phone, a tablet computer, etc.) for receiving display information associated with computer 402 via any communication means, including via the Internet and cloud-based networks. In addition to the monitor 444, a computer typically comprises other peripheral output devices (not shown), such as speakers, printers, etc.

The computer 402 can operate in a networked environment using logical connections via wired and/or wireless communications to one or more remote computers, such as a remote computer(s) 448. The remote computer(s) 448 can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically comprises many or all of the elements described relative to the computer 402, although, for purposes of brevity, only a remote memory/storage device 450 is illustrated. The logical connections depicted comprise wired/wireless connectivity to a local area network (LAN) 452 and/or larger networks, e.g., a wide area network (WAN) 454. Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, e.g., the Internet.

When used in a LAN networking environment, the computer 402 can be connected to the LAN 452 through a wired and/or wireless communication network interface or adapter 456. The adapter 456 can facilitate wired or wireless communication to the LAN 452, which can also comprise a wireless AP disposed thereon for communicating with the adapter 456.

When used in a WAN networking environment, the computer 402 can comprise a modem 458 or can be connected to a communications server on the WAN 454 or has other means for establishing communications over the WAN 454, such as by way of the Internet. The modem 458, which can be internal or external and a wired or wireless device, can be connected to the system bus 408 via the input device interface 442. In a networked environment, program modules depicted relative to the computer 402 or portions thereof, can be stored in the remote memory/storage device 450. It will be appreciated that the network connections shown are example and other means of establishing a communications link between the computers can be used.

The computer 402 can be operable to communicate with any wireless devices or entities operatively disposed in wireless communication, e.g., a printer, scanner, desktop and/or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, restroom), and telephone. This can comprise Wireless Fidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bed in a hotel room or a conference room at work, without wires. Wi-Fi is a wireless technology similar to that used in a cell phone that enables such devices, e.g., computers, to send and receive data indoors and out; anywhere within the range of a base station. Wi-Fi networks use radio technologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to provide secure, reliable, fast wireless connectivity. A Wi-Fi network can be used to connect computers to each other, to the Internet, and to wired networks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operate in the unlicensed 2.4 and 5 GHz radio bands for example or with products that contain both bands (dual band), so the networks can provide real-world performance similar to the basic 10BaseT wired Ethernet networks used in many offices.

Turning now to FIG. 5 , an embodiment 500 of a mobile network platform 510 is shown that is an example of network elements 150, 152, 154, 156, and/or VNEs 330, 332, 334, etc. For example, platform 510 can facilitate in whole or in part identifying a set of target users of user equipment communication devices (UEs), based on reports from the UEs regarding a quality of service (QoS) experienced by the respective UEs; obtaining from internal sources a set of key performance indicators (KPIs) for the communication network; correlating information received from external sources with the data obtained from the internal sources to validate the reports from the UEs; and recommending, in accordance with data records generated by the correlating, an action to improve the QoS for a UE of the set of UEs, where the action includes a modification of the UE and/or a reconfiguration of the network. In one or more embodiments, the mobile network platform 510 can generate and receive signals transmitted and received by base stations or access points such as base station or access point 122. Generally, mobile network platform 510 can comprise components, e.g., nodes, gateways, interfaces, servers, or disparate platforms, that facilitate both packet-switched (PS) (e.g., internet protocol (IP), frame relay, asynchronous transfer mode (ATM)) and circuit-switched (CS) traffic (e.g., voice and data), as well as control generation for networked wireless telecommunication. As a non-limiting example, mobile network platform 510 can be included in telecommunications carrier networks, and can be considered carrier-side components as discussed elsewhere herein. Mobile network platform 510 comprises CS gateway node(s) 512 which can interface CS traffic received from legacy networks like telephony network(s) 540 (e.g., public switched telephone network (PSTN), or public land mobile network (PLMN)) or a signaling system #7 (SS7) network 560. CS gateway node(s) 512 can authorize and authenticate traffic (e.g., voice) arising from such networks. Additionally, CS gateway node(s) 512 can access mobility, or roaming, data generated through SS7 network 560; for instance, mobility data stored in a visited location register (VLR), which can reside in memory 530. Moreover, CS gateway node(s) 512 interfaces CS-based traffic and signaling and PS gateway node(s) 518. As an example, in a 3GPP UMTS network, CS gateway node(s) 512 can be realized at least in part in gateway GPRS support node(s) (GGSN). It should be appreciated that functionality and specific operation of CS gateway node(s) 512, PS gateway node(s) 518, and serving node(s) 516, is provided and dictated by radio technology(ies) utilized by mobile network platform 510 for telecommunication over a radio access network 520 with other devices, such as a radiotelephone 575.

In addition to receiving and processing CS-switched traffic and signaling, PS gateway node(s) 518 can authorize and authenticate PS-based data sessions with served mobile devices. Data sessions can comprise traffic, or content(s), exchanged with networks external to the mobile network platform 510, like wide area network(s) (WANs) 550, enterprise network(s) 570, and service network(s) 580, which can be embodied in local area network(s) (LANs), can also be interfaced with mobile network platform 510 through PS gateway node(s) 518. It is to be noted that WANs 550 and enterprise network(s) 570 can embody, at least in part, a service network(s) like IP multimedia subsystem (IMS). Based on radio technology layer(s) available in technology resource(s) or radio access network 520, PS gateway node(s) 518 can generate packet data protocol contexts when a data session is established; other data structures that facilitate routing of packetized data also can be generated. To that end, in an aspect, PS gateway node(s) 518 can comprise a tunnel interface (e.g., tunnel termination gateway (TTG) in 3GPP UMTS network(s) (not shown)) which can facilitate packetized communication with disparate wireless network(s), such as Wi-Fi networks.

In embodiment 500 l, mobile network platform 510 also comprises serving node(s) 516 that, based upon available radio technology layer(s) within technology resource(s) in the radio access network 520, convey the various packetized flows of data streams received through PS gateway node(s) 518. It is to be noted that for technology resource(s) that rely primarily on CS communication, server node(s) can deliver traffic without reliance on PS gateway node(s) 518; for example, server node(s) can embody at least in part a mobile switching center. As an example, in a 3GPP UMTS network, serving node(s) 516 can be embodied in serving GPRS support node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s) 514 in mobile network platform 510 can execute numerous applications that can generate multiple disparate packetized data streams or flows, and manage (e.g., schedule, queue, format . . . ) such flows. Such application(s) can comprise add-on features to standard services (for example, provisioning, billing, customer support . . . ) provided by mobile network platform 510. Data streams (e.g., content(s) that are part of a voice call or data session) can be conveyed to PS gateway node(s) 518 for authorization/authentication and initiation of a data session, and to serving node(s) 516 for communication thereafter. In addition to application server, server(s) 514 can comprise utility server(s), a utility server can comprise a provisioning server, an operations and maintenance server, a security server that can implement at least in part a certificate authority and firewalls as well as other security mechanisms, and the like. In an aspect, security server(s) secure communication served through mobile network platform 510 to ensure network's operation and data integrity in addition to authorization and authentication procedures that CS gateway node(s) 512 and PS gateway node(s) 518 can enact. Moreover, provisioning server(s) can provision services from external network(s) like networks operated by a disparate service provider; for instance, WAN 550 or Global Positioning System (GPS) network(s) (not shown). Provisioning server(s) can also provision coverage through networks associated to mobile network platform 510 (e.g., deployed and operated by the same service provider), such as the distributed antennas networks shown in FIG. 1(s) that enhance wireless service coverage by providing more network coverage.

It is to be noted that server(s) 514 can comprise one or more processors configured to confer at least in part the functionality of mobile network platform 510. To that end, the one or more processor can execute code instructions stored in memory 530, for example. It is should be appreciated that server(s) 514 can comprise a content manager, which operates in substantially the same manner as described hereinbefore.

In example embodiment 500, memory 530 can store information related to operation of mobile network platform 510. Other operational information can comprise provisioning information of mobile devices served through mobile network platform 510, subscriber databases; application intelligence, pricing schemes, e.g., promotional rates, flat-rate programs, couponing campaigns; technical specification(s) consistent with telecommunication protocols for operation of disparate radio, or wireless, technology layers; and so forth. Memory 530 can also store information from at least one of telephony network(s) 540, WAN 550, SS7 network 560, or enterprise network(s) 570. In an aspect, memory 530 can be, for example, accessed as part of a data store component or as a remotely connected memory store.

In order to provide a context for the various aspects of the disclosed subject matter, FIG. 5 , and the following discussion, are intended to provide a brief, general description of a suitable environment in which the various aspects of the disclosed subject matter can be implemented. While the subject matter has been described above in the general context of computer-executable instructions of a computer program that runs on a computer and/or computers, those skilled in the art will recognize that the disclosed subject matter also can be implemented in combination with other program modules. Generally, program modules comprise routines, programs, components, data structures, etc. that perform particular tasks and/or implement particular abstract data types.

Turning now to FIG. 6 , an illustrative embodiment of a communication device 600 is shown. The communication device 600 can serve as an illustrative embodiment of devices such as data terminals 114, mobile devices 124, vehicle 126, display devices 144 or other client devices for communication via either communications network 125. For example, computing device 600 can facilitate in whole or in part identifying a set of target users of user equipment communication devices (UEs), based on reports from the UEs regarding a quality of service (QoS) experienced by the respective UEs; obtaining from internal sources a set of key performance indicators (KPIs) for the communication network; correlating information received from external sources with the data obtained from the internal sources to validate the reports from the UEs; and recommending, in accordance with data records generated by the correlating, an action to improve the QoS for a UE of the set of UEs, where the action includes a modification of the UE and/or a reconfiguration of the network.

The communication device 600 can comprise a wireline and/or wireless transceiver 602 (herein transceiver 602), a user interface (UI) 604, a power supply 614, a location receiver 616, a motion sensor 618, an orientation sensor 620, and a controller 606 for managing operations thereof. The transceiver 602 can support short-range or long-range wireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, or cellular communication technologies, just to mention a few (Bluetooth® and ZigBee® are trademarks registered by the Bluetooth® Special Interest Group and the ZigBee® Alliance, respectively). Cellular technologies can include, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO, WiMAX, SDR, LTE, as well as other next generation wireless communication technologies as they arise. The transceiver 602 can also be adapted to support circuit-switched wireline access technologies (such as PSTN), packet-switched wireline access technologies (such as TCP/IP, VoIP, etc.), and combinations thereof.

The UI 604 can include a depressible or touch-sensitive keypad 608 with a navigation mechanism such as a roller ball, a joystick, a mouse, or a navigation disk for manipulating operations of the communication device 600. The keypad 608 can be an integral part of a housing assembly of the communication device 600 or an independent device operably coupled thereto by a tethered wireline interface (such as a USB cable) or a wireless interface supporting for example Bluetooth®. The keypad 608 can represent a numeric keypad commonly used by phones, and/or a QWERTY keypad with alphanumeric keys. The UI 604 can further include a display 610 such as monochrome or color LCD (Liquid Crystal Display), OLED (Organic Light Emitting Diode) or other suitable display technology for conveying images to an end user of the communication device 600. In an embodiment where the display 610 is touch-sensitive, a portion or all of the keypad 608 can be presented by way of the display 610 with navigation features.

The display 610 can use touch screen technology to also serve as a user interface for detecting user input. As a touch screen display, the communication device 600 can be adapted to present a user interface having graphical user interface (GUI) elements that can be selected by a user with a touch of a finger. The display 610 can be equipped with capacitive, resistive or other forms of sensing technology to detect how much surface area of a user's finger has been placed on a portion of the touch screen display. This sensing information can be used to control the manipulation of the GUI elements or other functions of the user interface. The display 610 can be an integral part of the housing assembly of the communication device 600 or an independent device communicatively coupled thereto by a tethered wireline interface (such as a cable) or a wireless interface.

The UI 604 can also include an audio system 612 that utilizes audio technology for conveying low volume audio (such as audio heard in proximity of a human ear) and high volume audio (such as speakerphone for hands free operation). The audio system 612 can further include a microphone for receiving audible signals of an end user. The audio system 612 can also be used for voice recognition applications. The UI 604 can further include an image sensor 613 such as a charged coupled device (CCD) camera for capturing still or moving images.

The power supply 614 can utilize common power management technologies such as replaceable and rechargeable batteries, supply regulation technologies, and/or charging system technologies for supplying energy to the components of the communication device 600 to facilitate long-range or short-range portable communications. Alternatively, or in combination, the charging system can utilize external power sources such as DC power supplied over a physical interface such as a USB port or other suitable tethering technologies.

The location receiver 616 can utilize location technology such as a global positioning system (GPS) receiver capable of assisted GPS for identifying a location of the communication device 600 based on signals generated by a constellation of GPS satellites, which can be used for facilitating location services such as navigation. The motion sensor 618 can utilize motion sensing technology such as an accelerometer, a gyroscope, or other suitable motion sensing technology to detect motion of the communication device 600 in three-dimensional space. The orientation sensor 620 can utilize orientation sensing technology such as a magnetometer to detect the orientation of the communication device 600 (north, south, west, and east, as well as combined orientations in degrees, minutes, or other suitable orientation metrics).

The communication device 600 can use the transceiver 602 to also determine a proximity to a cellular, WiFi, Bluetooth®, or other wireless access points by sensing techniques such as utilizing a received signal strength indicator (RSSI) and/or signal time of arrival (TOA) or time of flight (TOF) measurements. The controller 606 can utilize computing technologies such as a microprocessor, a digital signal processor (DSP), programmable gate arrays, application specific integrated circuits, and/or a video processor with associated storage memory such as Flash, ROM, RAM, SRAM, DRAM or other storage technologies for executing computer instructions, controlling, and processing data supplied by the aforementioned components of the communication device 600.

Other components not shown in FIG. 6 can be used in one or more embodiments of the subject disclosure. For instance, the communication device 600 can include a slot for adding or removing an identity module such as a Subscriber Identity Module (SIM) card or Universal Integrated Circuit Card (UICC). SIM or UICC cards can be used for identifying subscriber services, executing programs, storing subscriber data, and so on.

The terms “first,” “second,” “third,” and so forth, as used in the claims, unless otherwise clear by context, is for clarity only and doesn't otherwise indicate or imply any order in time. For instance, “a first determination,” “a second determination,” and “a third determination,” does not indicate or imply that the first determination is to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “data store,” data storage,” “database,” and substantially any other information storage component relevant to operation and functionality of a component, refer to “memory components,” or entities embodied in a “memory” or components comprising the memory. It will be appreciated that the memory components described herein can be either volatile memory or nonvolatile memory, or can comprise both volatile and nonvolatile memory, by way of illustration, and not limitation, volatile memory, non-volatile memory, disk storage, and memory storage. Further, nonvolatile memory can be included in read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile memory can comprise random access memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM). Additionally, the disclosed memory components of systems or methods herein are intended to comprise, without being limited to comprising, these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can be practiced with other computer system configurations, comprising single-processor or multiprocessor computer systems, mini-computing devices, mainframe computers, as well as personal computers, hand-held computing devices (e.g., PDA, phone, smartphone, watch, tablet computers, netbook computers, etc.), microprocessor-based or programmable consumer or industrial electronics, and the like. The illustrated aspects can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network; however, some if not all aspects of the subject disclosure can be practiced on stand-alone computers. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

In one or more embodiments, information regarding use of services can be generated including services being accessed, media consumption history, user preferences, and so forth. This information can be obtained by various methods including user input, detecting types of communications (e.g., video content vs. audio content), analysis of content streams, sampling, and so forth. The generating, obtaining and/or monitoring of this information can be responsive to an authorization provided by the user. In one or more embodiments, an analysis of data can be subject to authorization from user(s) associated with the data, such as an opt-in, an opt-out, acknowledgement requirements, notifications, selective authorization based on types of data, and so forth.

Some of the embodiments described herein can also employ artificial intelligence (AI) to facilitate automating one or more features described herein. The embodiments (e.g., in connection with automatically identifying acquired cell sites that provide a maximum value/benefit after addition to an existing communication network) can employ various AI-based schemes for carrying out various embodiments thereof. Moreover, the classifier can be employed to determine a ranking or priority of each cell site of the acquired network. A classifier is a function that maps an input attribute vector, x=(x1, x2, x3, x4, . . . , xn), to a confidence that the input belongs to a class, that is, f(x)=confidence (class). Such classification can employ a probabilistic and/or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to determine or infer an action that a user desires to be automatically performed. A support vector machine (SVM) is an example of a classifier that can be employed. The SVM operates by finding a hypersurface in the space of possible inputs, which the hypersurface attempts to split the triggering criteria from the non-triggering events. Intuitively, this makes the classification correct for testing data that is near, but not identical to training data. Other directed and undirected model classification approaches comprise, e.g., naïve Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, and probabilistic classification models providing different patterns of independence can be employed. Classification as used herein also is inclusive of statistical regression that is utilized to develop models of priority.

As will be readily appreciated, one or more of the embodiments can employ classifiers that are explicitly trained (e.g., via a generic training data) as well as implicitly trained (e.g., via observing UE behavior, operator preferences, historical information, receiving extrinsic information). For example, SVMs can be configured via a learning or training phase within a classifier constructor and feature selection module. Thus, the classifier(s) can be used to automatically learn and perform a number of functions, including but not limited to determining according to predetermined criteria which of the acquired cell sites will benefit a maximum number of subscribers and/or which of the acquired cell sites will add minimum value to the existing communication network coverage, etc.

As used in some contexts in this application, in some embodiments, the terms “component,” “system” and the like are intended to refer to, or comprise, a computer-related entity or an entity related to an operational apparatus with one or more specific functionalities, wherein the entity can be either hardware, a combination of hardware and software, software, or software in execution. As an example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, computer-executable instructions, a program, and/or a computer. By way of illustration and not limitation, both an application running on a server and the server can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components may communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems via the signal). As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry, which is operated by a software or firmware application executed by a processor, wherein the processor can be internal or external to the apparatus and executes at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts, the electronic components can comprise a processor therein to execute software or firmware that confers at least in part the functionality of the electronic components. While various components have been illustrated as separate components, it will be appreciated that multiple components can be implemented as a single component, or a single component can be implemented as multiple components, without departing from example embodiments.

Further, the various embodiments can be implemented as a method, apparatus or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware or any combination thereof to control a computer to implement the disclosed subject matter. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device or computer-readable storage/communications media. For example, computer readable storage media can include, but are not limited to, magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips), optical disks (e.g., compact disk (CD), digital versatile disk (DVD)), smart cards, and flash memory devices (e.g., card, stick, key drive). Of course, those skilled in the art will recognize many modifications can be made to this configuration without departing from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to mean serving as an instance or illustration. Any embodiment or design described herein as “example” or “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word example or exemplary is intended to present concepts in a concrete fashion. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. In addition, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,” subscriber station,” “access terminal,” “terminal,” “handset,” “mobile device” (and/or terms representing similar terminology) can refer to a wireless device utilized by a subscriber or user of a wireless communication service to receive or convey data, control, voice, video, sound, gaming or substantially any data-stream or signaling-stream. The foregoing terms are utilized interchangeably herein and with reference to the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” and the like are employed interchangeably throughout, unless context warrants particular distinctions among the terms. It should be appreciated that such terms can refer to human entities or automated components supported through artificial intelligence (e.g., a capacity to make inference based, at least, on complex mathematical formalisms), which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially any computing processing unit or device comprising, but not limited to comprising, single-core processors; single-processors with software multithread execution capability; multi-core processors; multi-core processors with software multithread execution capability; multi-core processors with hardware multithread technology; parallel platforms; and parallel platforms with distributed shared memory. Additionally, a processor can refer to an integrated circuit, an application specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic controller (PLC), a complex programmable logic device (CPLD), a discrete gate or transistor logic, discrete hardware components or any combination thereof designed to perform the functions described herein. Processors can exploit nano-scale architectures such as, but not limited to, molecular and quantum-dot based transistors, switches and gates, in order to optimize space usage or enhance performance of user equipment. A processor can also be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,” and substantially any other information storage component relevant to operation and functionality of a component, refer to “memory components,” or entities embodied in a “memory” or components comprising the memory. It will be appreciated that the memory components or computer-readable storage media, described herein can be either volatile memory or nonvolatile memory or can include both volatile and nonvolatile memory.

What has been described above includes mere examples of various embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing these examples, but one of ordinary skill in the art can recognize that many further combinations and permutations of the present embodiments are possible. Accordingly, the embodiments disclosed and/or claimed herein are intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims. Furthermore, to the extent that the term “includes” is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim.

In addition, a flow diagram may include a “start” and/or “continue” indication. The “start” and “continue” indications reflect that the steps presented can optionally be incorporated in or otherwise used in conjunction with other routines. In this context, “start” indicates the beginning of the first step presented and may be preceded by other activities not specifically shown. Further, the “continue” indication reflects that the steps presented may be performed multiple times and/or may be succeeded by other activities not specifically shown. Further, while a flow diagram indicates a particular ordering of steps, other orderings are likewise possible provided that the principles of causality are maintained.

As may also be used herein, the term(s) “operably coupled to”, “coupled to”, and/or “coupling” includes direct coupling between items and/or indirect coupling between items via one or more intervening items. Such items and intervening items include, but are not limited to, junctions, communication paths, components, circuit elements, circuits, functional blocks, and/or devices. As an example of indirect coupling, a signal conveyed from a first item to a second item may be modified by one or more intervening items by modifying the form, nature or format of information in a signal, while one or more elements of the information in the signal are nevertheless conveyed in a manner than can be recognized by the second item. In a further example of indirect coupling, an action in a first item can cause a reaction on the second item, as a result of actions and/or reactions in one or more intervening items.

Although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement which achieves the same or similar purpose may be substituted for the embodiments described or shown by the subject disclosure. The subject disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, can be used in the subject disclosure. For instance, one or more features from one or more embodiments can be combined with one or more features of one or more other embodiments. In one or more embodiments, features that are positively recited can also be negatively recited and excluded from the embodiment with or without replacement by another structural and/or functional feature. The steps or functions described with respect to the embodiments of the subject disclosure can be performed in any order. The steps or functions described with respect to the embodiments of the subject disclosure can be performed alone or in combination with other steps or functions of the subject disclosure, as well as from other embodiments or from other steps that have not been described in the subject disclosure. Further, more than or less than all of the features described with respect to an embodiment can also be utilized. 

What is claimed is:
 1. A method comprising: identifying, by a processing system including a processor, a set of target users of a communication network having associated therewith a set of user equipment communication devices (UEs), wherein the target users are identified based on reports from the UEs regarding a quality of service (QoS) experienced by the respective UEs, user input received via the UEs, information regarding the target users received from external sources that are external to the processing system, or a combination thereof; obtaining, by the processing system from internal sources that are internal to the communication network, data for the communication network comprising a set of key performance indicators (KPIs); correlating, by the processing system, the information received from the external sources with the data obtained from the internal sources to validate the reports from the UEs, resulting in correlated data for each of the UEs; generating, by the processing system from the correlated data, data records relating to the respective UEs; recommending, by the processing system in accordance with the data records, an action to improve the QoS for a UE of the set of UEs, the action comprising a modification of the UE, a reconfiguration of the network, or a combination thereof; informing, by the processing system via the respective UEs, the target users regarding the action; receiving, by the processing system, communications from at least a portion of the set of UEs regarding the action; and analyzing, by the processing system, the communications to provide a revision of the recommended action using a machine learning model, the machine learning model generated at least in part using artificial intelligence techniques.
 2. The method of claim 1, wherein the information received from the external sources comprises a posting via a social network by at least one of the set of target users.
 3. The method of claim 1, further comprising storing, by the processing system, the data records in a graph database.
 4. The method of claim 1, wherein the recommending is performed in near real time with respect to the reports from the UEs and the data from the communication network.
 5. The method of claim 1, wherein the processor comprises a network edge processor.
 6. The method of claim 1, further comprising: collecting, by the processing system for a UE of the set of UEs, event data for the UE over a predetermined time interval, resulting in a UE record for the time interval; and generating, by the processing system, a time series of UE records for successive time intervals.
 7. The method of claim 6, wherein the communication network comprises a cellular network, and wherein the event data comprises a geolocation of the UE, cells visited by the UE, and records of one or more of a UE context release, a UE measurement, a radio resource control (RRC) measurement, and a resource block (RB) traffic measurement.
 8. The method of claim 1, further comprising: analyzing, by the processing system, event data for a UE of the set of UEs to detect a performance abnormality of the UE.
 9. The method of claim 8, wherein the event data comprise records of lost connections, radio link failures, timeouts of UE capability enquiries, or a combination thereof.
 10. The method of claim 1, further comprising classifying, by the processing system, the set of UEs according to a type of problem or abnormality experienced by the respective UEs, resulting in a classification of the set of UEs.
 11. The method of claim 10, further comprising analyzing, by the processing system, the data records to address the problem or abnormality in accordance with the classification, a location, and a trajectory for each of the UEs respectively.
 12. The method of claim 1, wherein the communication network comprises a cellular network, and wherein the action comprises adding a new cell to the cellular network.
 13. A device, comprising: a processing system including a network edge processor of a communication network; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising: identifying a set of target users of the communication network having associated therewith a set of user equipment communication devices (UEs), wherein the target users are identified based on reports from the UEs regarding a quality of service (QoS) experienced by the respective UEs, user input received via the UEs, information regarding the target users received from external sources that are external to the processing system, or a combination thereof; obtaining, from internal sources that are internal to the communication network, data for the communication network comprising a set of key performance indicators (KPIs); correlating the information received from the external sources with the data obtained from the internal sources to validate the reports from the UEs, resulting in correlated data for each of the UEs; generating, from the correlated data, data records relating to the respective UEs; recommending, in accordance with the data records, an action to improve the QoS for a UE of the set of UEs, the action comprising a modification of the UE, a reconfiguration of the network, or a combination thereof; informing, via the respective UEs, the target users regarding the action; receiving communications from at least a portion of the set of UEs regarding the action; and analyzing the communications to provide a revision of the recommended action using a machine learning model, the machine learning model generated at least in part using artificial intelligence techniques.
 14. The device of claim 13, wherein the information received from the external sources comprises a posting via a social network by at least one of the set of target users.
 15. The device of claim 13, wherein the recommending is performed in near real time with respect to the reports from the UEs and the data from the communication network.
 16. The device of claim 13, wherein the operations further comprise: collecting event data for a UE of the set of UEs over a predetermined time interval, resulting in a UE record for the time interval; and generating a time series of UE records for successive time intervals.
 17. The device of claim 16, wherein the communication network comprises a cellular network, and wherein the event data comprises a geolocation of the UE, cells visited by the UE, and records of one or more of a UE context release, a UE measurement, a radio resource control (RRC) measurement, and a resource block (RB) traffic measurement.
 18. A non-transitory machine-readable medium comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising: identifying a set of target users of the communication network having associated therewith a set of user equipment communication devices (UEs), wherein the target users are identified based on reports from the UEs regarding a quality of service (QoS) experienced by the respective UEs, user input received via the UEs, information regarding the target users received from external sources that are external to the processing system, or a combination thereof; obtaining, from internal sources that are internal to the communication network, data for the communication network comprising a set of key performance indicators (KPIs); correlating the information received from the external sources with the data obtained from the internal sources to validate the reports from the UEs, resulting in correlated data for each of the UEs; generating, from the correlated data, data records relating to the respective UEs; storing the data records in a graph database; recommending, in accordance with the data records, an action to improve the QoS for a UE of the set of UEs, the action comprising a modification of the UE, a reconfiguration of the network, or a combination thereof; informing, via the respective UEs, the target users regarding the action; receiving communications from at least a portion of the set of UEs regarding the action; and analyzing the communications to provide a revision of the recommended action using a machine learning model, the machine learning model generated at least in part using artificial intelligence techniques.
 19. The non-transitory machine-readable medium of claim 18, wherein the information received from the external sources comprises a posting via a social network by at least one of the set of target users.
 20. The non-transitory machine-readable medium of claim 18, wherein the recommending is performed in near real time with respect to the reports from the UEs and the data from the communication network. 